Detection and recognition of objects in an image typically involves image filtering and pattern recognition techniques. Detecting and recognizing objects in an image containing hundreds or thousands of pixels may require extensive processing power and may be time consuming. Therefore, it may be useful to reduce the dimensionality of an image before processing the image to detect and recognize objects in the image. One known technique for reducing dimensionality of data is Principal Component Analysis (PCA). PCA is described in, for example, Joliffe I. T., Principal Component Analysis, Springer-Verlag, New York (1986).
PCA uses the eigenvalues and eigenvectors of the covariance matrix of a set of data as representative of valuable features of the set of data, thereby reducing the dimensionality of the set of data.
There exist many methods and mechanisms that enable humans to interact with computers. Computer vision technologies may allow a computer to detect an object within an image captured by a camera. A computer that is capable of detecting and recognizing an object within an image may provide a user with the ability to interact with the computer through the use of hand gestures.
A computer-user interface may be displayed on a surface or screen. One or more cameras may monitor activity in the vicinity of the surface or screen and capture images of the activity in the vicinity of the screen. The computer may then process these images, detect one or more objects within the images, and perceive that a user is using hand gestures to interact with the computer-user interface displayed on the surface or screen.
Some systems attempt to perceive that a user is using hand gestures to interact with the computer-user interface displayed on the surface or screen. Some of these systems simply perceive the brightest object in an image and classify that object as a hand or finger. Consequently, these systems may perceive an object as a hand or finger even though the object is neither a hand nor a finger.